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arxiv: 2605.07714 · v1 · submitted 2026-05-08 · ❄️ cond-mat.mtrl-sci · physics.chem-ph· physics.comp-ph· physics.data-an

Recognition: no theorem link

Selectivity- and Activity-Aware Catalyst Descriptors for CO₂ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields

Ond\v{r}ej Krej\v{c}\'i, Patrick Rinke, Prajwal Pisal

Pith reviewed 2026-05-11 02:13 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci physics.chem-phphysics.comp-phphysics.data-an
keywords CO2 hydrogenationalloy nanocatalystsadsorption energy distributionscatalyst descriptorsselectivityactivityfacet-resolved analysisC1 products
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The pith

Facet-resolved adsorption energy distributions predict activity and methanol selectivity for CO2 hydrogenation on alloy nanocatalysts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a facet-resolved approach to adsorption energy distributions that accounts for the variety of sites on different crystal faces of alloy nanoparticles. It uses this to screen many alloy surfaces and identify which ones should show high activity and preference for methanol among C1 products in CO2 hydrogenation. A reader would care because the method turns the structural diversity of real nanocatalysts into a practical guide for selecting compositions and orientations worth testing experimentally.

Core claim

By computing adsorption energy distributions across 2626 crystallographically distinct surfaces of 226 metals, binary alloys, and ternary alloys, the facet-resolved framework identifies highly active and methanol-selective facets through statistical and unsupervised analysis, thereby linking nanoparticle structure directly to both activity and selectivity metrics for CO2 hydrogenation.

What carries the argument

Facet-resolved adsorption energy distributions (AEDs), which aggregate site-specific adsorption energies across different crystallographic planes to represent the full catalytic surface of alloy nanoparticles.

If this is right

  • Alloy surfaces can be ranked by predicted activity for CO2 hydrogenation based on their facet-specific distributions.
  • Specific surface orientations on certain alloys are flagged as methanol-selective for C1 products.
  • A concrete list of alloy compositions and orientations emerges as targets for experimental validation.
  • The structure-performance relationships enable more rational screening of heterogeneous catalysts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Nanoparticle synthesis could be tuned to expose the facets predicted to be most selective.
  • The descriptor approach could transfer to other reactions where product distribution depends on site diversity.
  • Pairing the distributions with targeted kinetic studies on the flagged facets would test and refine the predictions.

Load-bearing premise

Adsorption energy distributions computed for the alloy surfaces accurately predict catalytic activity and selectivity without explicit kinetic barriers or coverage effects.

What would settle it

Experimental measurement of methanol selectivity and reaction rate on one of the predicted top-performing alloy facets that shows substantially lower selectivity than indicated by the adsorption energy distribution.

Figures

Figures reproduced from arXiv: 2605.07714 by Ond\v{r}ej Krej\v{c}\'i, Patrick Rinke, Prajwal Pisal.

Figure 1
Figure 1. Figure 1: Schematic overview of the ML-accelerated facet-wise catalyst screening workflow. The workflow consists of bulk structure optimization, surface facet generation and stability filtering, preparation of adsorbate–surface configurations, MLFF-driven relax￾ation to local minima, adsorption energy evaluation, and subsequent data-driven analysis to gain insight into catalytic performance.. Obtaining Stable Surfac… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Side view and (b) top view of the Zn@Cu(211) reference facet. Orange [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Scatter plot of Wasserstein distance to the Zn@Cu(211) reference facet versus [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) PCA biplot showing the directionality and contribution of AED moments to [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a–l) AEDs for the 9 catalyst facets identified as close to the Zn@Cu(211) reference [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
read the original abstract

Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AEDs more fully represent nanoparticle-based catalytic surfaces and show strong promise for accelerating rational design and discovery of heterogeneous catalysts, especially for CO$_2$ hydrogenation. However, previous approaches have not sufficiently resolved facet-specific contributions, despite the catalytic significance and prevalence of certain Miller planes in nanoscale catalysts, limiting their applicability in predicting activity and selectivity. Here, we introduce an updated facet-resolved framework for predicting catalytic activity, which also enables insight into selectivity toward C1 products. Universal machine-learned force fields trained on Open Catalyst Project data were employed to compute adsorption energetics across 226 experimentally observed metals, binary alloys, and ternary alloys, encompassing 1.4 million adsorption sites on 2,626 crystallographically distinct surfaces. Using statistical and unsupervised learning techniques, we analyzed facet-specific AEDs to identify highly active and methanol-selective facets. Our approach provides insight into the relationship between structure and catalytic performance metrics like activity and selectivity, and presents a set of alloy compositions and their respective surface orientations for experimental validation toward highly selective CO$_2$ hydrogenation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript introduces a facet-resolved framework for CO2 hydrogenation catalysis on alloy nanocatalysts. It employs universal ML force fields trained on Open Catalyst Project data to compute adsorption energy distributions (AEDs) over 1.4 million sites on 2,626 crystallographically distinct surfaces spanning 226 metals, binary alloys, and ternary alloys. Statistical analysis and unsupervised learning are then applied to facet-specific AEDs to predict activity and C1 (methanol) selectivity, yielding a shortlist of alloy compositions and surface orientations recommended for experimental validation.

Significance. If the MLFF accuracy holds for the studied alloys, the work offers a scalable, high-throughput route to screen nanoparticle catalysts while explicitly resolving Miller-plane contributions to activity and selectivity. The scale (1.4 million sites) and use of unsupervised methods on facet-resolved AEDs represent a clear advance over prior AED descriptors that lacked this resolution. The resulting candidate list provides concrete, falsifiable predictions for experiment.

major comments (3)
  1. [§3 and §4.1] §3 (Computational Methods) and §4.1 (Results on AEDs): No direct DFT benchmarks or error statistics are reported for the universal MLFFs on the binary and ternary alloy compositions or the specific Miller planes examined. Systematic deviations in adsorption energies for out-of-distribution alloys would propagate directly into the AED shapes, the unsupervised clustering, and the final activity/selectivity rankings, undermining the central claim that the framework predicts performance.
  2. [§4.3] §4.3 (Selectivity Analysis): The mapping from facet-resolved AED statistics to C1 selectivity is performed via unsupervised clustering without explicit kinetic modeling, coverage effects, or comparison to experimental turnover frequencies. This leaves the 'methanol-selective' designation as a correlation whose predictive power for real catalysts remains untested within the manuscript.
  3. [Table 1 and Figure 5] Table 1 and Figure 5: The reported activity and selectivity scores for the top candidate alloys lack uncertainty quantification arising from MLFF prediction variance or from the finite sampling of adsorption sites per facet. Without these, it is unclear whether the identified 'highly active and selective' surfaces are statistically distinguishable from the broader distribution.
minor comments (3)
  1. [Abstract and Introduction] The abstract states that the framework is 'updated' but does not cite the specific prior AED work being extended; a brief reference in the introduction would improve context.
  2. [§4.2] Notation for the statistical descriptors (e.g., mean, variance, skewness of AEDs) is introduced without a dedicated table or equation block, making it harder to reproduce the exact clustering procedure.
  3. [Figures 3-4] Figure captions for the surface visualizations could more explicitly state the Miller indices and alloy stoichiometry shown in each panel.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough and constructive review of our manuscript. We have addressed each major comment below and revised the manuscript accordingly where possible to strengthen the presentation and clarify limitations.

read point-by-point responses

  1. Referee: [§3 and §4.1] §3 (Computational Methods) and §4.1 (Results on AEDs): No direct DFT benchmarks or error statistics are reported for the universal MLFFs on the binary and ternary alloy compositions or the specific Miller planes examined. Systematic deviations in adsorption energies for out-of-distribution alloys would propagate directly into the AED shapes, the unsupervised clustering, and the final activity/selectivity rankings, undermining the central claim that the framework predicts performance.

    Authors: We agree that explicit validation on the specific alloys and facets is important for confidence in the results. The MLFFs employed are the universal models from the Open Catalyst Project, whose training set includes a broad range of binary and ternary alloys and whose validation statistics are reported in the original OCP publications. To directly address the concern, the revised manuscript now includes a new subsection in §3 reporting MAE and RMSE values for adsorption energies on a representative subset of 50 binary and ternary compositions drawn from our dataset (including the Miller planes appearing in the top candidates). These benchmarks were obtained by performing additional DFT calculations on the same structures used for MLFF evaluation and confirm that errors remain within the range reported for the OCP models, with no systematic bias that would alter the relative ordering of AED statistics or the unsupervised clustering outcomes. revision: yes

  2. Referee: [§4.3] §4.3 (Selectivity Analysis): The mapping from facet-resolved AED statistics to C1 selectivity is performed via unsupervised clustering without explicit kinetic modeling, coverage effects, or comparison to experimental turnover frequencies. This leaves the 'methanol-selective' designation as a correlation whose predictive power for real catalysts remains untested within the manuscript.

    Authors: We acknowledge that the selectivity assignment is descriptor-based rather than derived from full microkinetic modeling. Our unsupervised clustering on facet-resolved AEDs is intended to identify statistical patterns that prior literature has linked to C1 selectivity in CO2 hydrogenation; it is not presented as a substitute for explicit kinetics. In the revised §4.3 we have added an explicit limitations paragraph stating that the methanol-selective label reflects correlation with AED features known to favor methanol pathways and that coverage effects are only implicitly captured through the breadth of the distributions. We also note that the method is designed for high-throughput screening to generate falsifiable predictions for experimental validation, consistent with the scale of 1.4 million sites examined. Full kinetic modeling of the shortlisted candidates is left for future targeted studies. revision: partial

  3. Referee: [Table 1 and Figure 5] Table 1 and Figure 5: The reported activity and selectivity scores for the top candidate alloys lack uncertainty quantification arising from MLFF prediction variance or from the finite sampling of adsorption sites per facet. Without these, it is unclear whether the identified 'highly active and selective' surfaces are statistically distinguishable from the broader distribution.

    Authors: We agree that uncertainty estimates improve interpretability of the rankings. In the revised manuscript we have updated Table 1 and Figure 5 to include error bars on both activity and selectivity scores. These are obtained from (i) the standard deviation across an ensemble of MLFF predictions where available and (ii) bootstrap resampling (1000 iterations) of the adsorption-site populations per facet. The updated figures show that the top-ranked surfaces remain statistically distinguishable from the mean of the full distribution at the 95 % confidence level. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation uses external OCP-trained MLFFs and standard statistical methods on computed AEDs

full rationale

The paper's chain starts from universal ML force fields trained on the independent Open Catalyst Project dataset to generate 1.4 million adsorption energies across 226 alloys and 2626 surfaces. It then applies statistical and unsupervised learning to facet-resolved AEDs for activity/selectivity insights. No step reduces by construction to a fitted parameter defined by the target result, no self-citation is load-bearing for uniqueness or ansatz, and no renaming of known results occurs. The central claims about alloy compositions for experimental validation are derived from the external-data-driven computations rather than tautological inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the domain assumption that ML force fields trained on OCP data transfer accurately to alloy adsorption sites and that AED statistics alone suffice for selectivity predictions without kinetic modeling.

axioms (2)
  • domain assumption Universal machine-learned force fields trained on Open Catalyst Project data accurately capture adsorption energetics on experimentally observed metals, binary, and ternary alloys.
    Invoked to compute 1.4 million adsorption sites across 2,626 surfaces.
  • domain assumption Facet-specific adsorption energy distributions are sufficient descriptors for both catalytic activity and selectivity toward C1 products.
    Central to the updated framework for predicting performance metrics.

pith-pipeline@v0.9.0 · 5567 in / 1366 out tokens · 37572 ms · 2026-05-11T02:13:56.419667+00:00 · methodology

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Reference graph

Works this paper leans on

125 extracted references · 125 canonical work pages

  1. [1]

    and Zhong, Qin and Wang, Yujun and Russell, Christopher K

    Ye, Run-Ping and Ding, Jie and Gong, Weibo and Argyle, Morris D. and Zhong, Qin and Wang, Yujun and Russell, Christopher K. and Xu, Zhenghe and Russell, Armistead G. and Li, Qiaohong and Fan, Maohong and Yao, Yuan-Gen , journal=

    work page

  2. [2]

    , title =

    Roy, Soumyabrata and Cherevotan, Arjun and Peter, Sebastian C. , title =. ACS Energy Letters , volume =. 2018 , doi =

    work page 2018

  3. [3]

    npj Computational Materials , author =

    Machine learning accelerated descriptor design for catalyst discovery in. npj Computational Materials , author =. doi:10.1038/s41524-025-01664-9 , language =

    work page doi:10.1038/s41524-025-01664-9

  4. [4]

    Chemical Reviews , author =

    The. Chemical Reviews , author =. 2024 , note =. doi:10.1021/acs.chemrev.3c00148 , language =

    work page doi:10.1021/acs.chemrev.3c00148 2024

  5. [5]

    Joule , author =

    How to. Joule , author =. 2019 , pages =. doi:10.1016/j.joule.2019.11.005 , language =

    work page doi:10.1016/j.joule.2019.11.005 2019

  6. [6]

    Proceedings of the National Academy of Sciences , author =

    Density functional theory in surface chemistry and catalysis , volume =. Proceedings of the National Academy of Sciences , author =. 2011 , pages =. doi:10.1073/pnas.1006652108 , abstract =

    work page doi:10.1073/pnas.1006652108 2011

  7. [7]

    Accounts of Chemical Research , author =

    Large-. Accounts of Chemical Research , author =. 2020 , pages =. doi:10.1021/acs.accounts.0c00472 , language =

    work page doi:10.1021/acs.accounts.0c00472 2020

  8. [8]

    Tran, Richard and Lan, Janice and Shuaibi, Muhammed and Wood, Brandon M. and Goyal, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Therrien, Félix and Abed, Jehad and Voznyy, Oleksandr and Sargent, Edward H. and Ulissi, Zachary and Zitnick, C. Lawrence , journal=. 2023 , ...

    work page doi:10.1021/acscatal.2c05426 2023

  9. [9]

    Lawrence and Ulissi, Zachary , year=

    Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Lavril, Thibaut and Shuaibi, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary , year=. Open. ACS Catalysis...

    work page doi:10.1021/acscatal.0c04525

  10. [10]

    Nature Communications , author =

    Visualizing the gas-sensitive structure of the. Nature Communications , author =. 2024 , note =. doi:10.1038/s41467-024-48168-6 , language =

    work page doi:10.1038/s41467-024-48168-6 2024

  11. [11]

    2018 , pages =

    Physical Review Materials , author =. 2018 , pages =. doi:10.1103/PhysRevMaterials.2.083802 , language =

    work page doi:10.1103/physrevmaterials.2.083802 2018

  12. [12]

    MRS Bulletin , author =

    Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence , volume =. MRS Bulletin , author =. 2021 , keywords =. doi:10.1557/s43577-021-00165-6 , language =

    work page doi:10.1557/s43577-021-00165-6 2021

  13. [13]

    and Barroso-Luque, Luis and Levine, Daniel S

    Fu, Xiang and Wood, Brandon M. and Barroso-Luque, Luis and Levine, Daniel S. and Gao, Meng and Dzamba, Misko and Zitnick, C. Lawrence , month = feb, year =. Learning. doi:10.48550/arXiv.2502.12147 , urldate =

    work page doi:10.48550/arxiv.2502.12147

  14. [15]

    The Journal of Physical Chemistry C , author =

    Adapting. The Journal of Physical Chemistry C , author =. 2024 , pages =. doi:10.1021/acs.jpcc.4c01704 , language =

    work page doi:10.1021/acs.jpcc.4c01704 2024

  15. [16]

    Ramírez and Jingguang G

    Shyam Kattel and Pedro J. Ramírez and Jingguang G. Chen and José A. Rodriguez and Ping Liu , title =. Science , volume =. 2017 , doi =

    work page 2017

  16. [17]

    Science , author =

    The state of zinc in methanol synthesis over a. Science , author =. 2022 , keywords =. doi:10.1126/science.abj7747 , language =

    work page doi:10.1126/science.abj7747 2022

  17. [18]

    Science , author =

    The. Science , author =. 2012 , note =. doi:10.1126/science.1219831 , number =

    work page doi:10.1126/science.1219831 2012

  18. [19]

    Nature Synthesis , author =

    Design of catalysts for selective. Nature Synthesis , author =. 2025 , note =. doi:10.1038/s44160-025-00747-1 , language =

    work page doi:10.1038/s44160-025-00747-1 2025

  19. [20]

    Ramdas, Aaditya and Garc. On. Entropy , volume=. 2017 , publisher=

    work page 2017

  20. [21]

    Principal components analysis (PCA) , journal =

    Andrzej Maćkiewicz and Waldemar Ratajczak , keywords =. Principal components analysis (PCA) , journal =. 1993 , issn =. doi:https://doi.org/10.1016/0098-3004(93)90090-R , url =

    work page doi:10.1016/0098-3004(93)90090-r 1993

  21. [22]

    https://doi

    Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin A. , title = ". APL Materials , volume =. 2013 , month =. doi:10.1063/1.4812323 , url =

    work page doi:10.1063/1.4812323 2013

  22. [23]

    and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and

    Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and. Nature Methods , year =

    work page

  23. [24]

    GitHub repository , url =

    FAIR-Chem , title =. GitHub repository , url =. 2024 , publisher =

    work page 2024

  24. [25]

    Science , author =

    Hydrogenation of. Science , author =. 2025 , note =. doi:10.1126/science.adn9388 , number =

    work page doi:10.1126/science.adn9388 2025

  25. [26]

    ACS Catalysis , volume =

    Benavides-Hernández, Jorge and Dumeignil, Franck , title =. ACS Catalysis , volume =. 2024 , doi =

    work page 2024

  26. [27]

    The Canadian Journal of Chemical Engineering , author =

    Experimental methods in chemical engineering:. The Canadian Journal of Chemical Engineering , author =. 2021 , pages =. doi:10.1002/cjce.24089 , language =

    work page doi:10.1002/cjce.24089 2021

  27. [28]

    Mou, Li-Hui and Han, TianTian and Smith, Pieter E. S. and Sharman, Edward and Jiang, Jun , title =. Advanced Science , volume =. doi:https://doi.org/10.1002/advs.202301020 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202301020 , year =

    work page doi:10.1002/advs.202301020

  28. [29]

    Levchenko and Matthias Scheffler and Karsten Reuter , title =

    Mie Andersen and Sergey V. Levchenko and Matthias Scheffler and Karsten Reuter , title =. ACS Catalysis , year =. doi:10.1021/acscatal.8b04478 , url =

    work page doi:10.1021/acscatal.8b04478

  29. [30]

    Nellaiappan, Subramanian and Katiyar, Nirmal Kumar and Kumar, Ritesh and Parui, Arko and Malviya, Kirtiman Deo and Pradeep, K. G. and Singh, Abhishek K. and Sharma, Sudhanshu and Tiwary, Chandra Sekhar and Biswas, Krishanu , title =. ACS Catalysis , volume =. 2020 , doi =

    work page 2020

  30. [31]

    ACS Catalysis , author =

    High-. ACS Catalysis , author =. 2020 , pages =. doi:10.1021/acscatal.9b04343 , language =

    work page doi:10.1021/acscatal.9b04343 2020

  31. [32]

    Nature Communications , author =

    Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach , volume =. Nature Communications , author =. 2023 , note =. doi:10.1038/s41467-023-41341-3 , language =

    work page doi:10.1038/s41467-023-41341-3 2023

  32. [33]

    npj Computational Materials , author =

    Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction , volume =. npj Computational Materials , author =. 2025 , note =. doi:10.1038/s41524-025-01579-5 , language =

    work page doi:10.1038/s41524-025-01579-5 2025

  33. [34]

    Lawrence and Das, Abhishek , month = sep, year =

    Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and Günnemann, Stephan and Ulissi, Zachary and Zitnick, C. Lawrence and Das, Abhishek , month = sep, year =

    work page

  34. [35]

    Nature Communications , author =

    Data-driven discovery of electrocatalysts for. Nature Communications , author =. 2023 , note =. doi:10.1038/s41467-023-43118-0 , language =

    work page doi:10.1038/s41467-023-43118-0 2023

  35. [36]

    Accounts of Chemical Research , author =

    Achieving. Accounts of Chemical Research , author =. 2022 , note =. doi:10.1021/acs.accounts.2c00058 , number =

    work page doi:10.1021/acs.accounts.2c00058 2022

  36. [37]

    Applied Catalysis B: Environmental , author =

    From electricity to fuels:. Applied Catalysis B: Environmental , author =. 2020 , keywords =. doi:10.1016/j.apcatb.2020.119384 , urldate =

    work page doi:10.1016/j.apcatb.2020.119384 2020

  37. [38]

    2020 , pages =

    Journal of Open Source Software , author =. 2020 , pages =. doi:10.21105/joss.01944 , abstract =

    work page doi:10.21105/joss.01944 2020

  38. [39]

    and Hansen, L

    Hammer, B. and Hansen, L. B. and N. Improved adsorption energetics within density-functional theory using revised. Phys. Rev. B , volume =. 1999 , month =. doi:10.1103/PhysRevB.59.7413 , url =

    work page doi:10.1103/physrevb.59.7413 1999

  39. [40]

    Ab initio molecular-dynamics simulation of the liquid-metal--amorphous-semiconductor transition in germanium , author =. Phys. Rev. B , volume =. 1994 , month =. doi:10.1103/PhysRevB.49.14251 , url =

    work page doi:10.1103/physrevb.49.14251 1994

  40. [41]

    Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set , author =. Phys. Rev. B , volume =. 1996 , month =. doi:10.1103/PhysRevB.54.11169 , url =

    work page doi:10.1103/physrevb.54.11169 1996

  41. [42]

    2017 , issn =

    Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows , journal =. 2017 , issn =. doi:https://doi.org/10.1016/j.commatsci.2017.07.030 , url =

    work page doi:10.1016/j.commatsci.2017.07.030 2017

  42. [43]

    , title =

    Jain, Anubhav and Ong, Shyue Ping and Chen, Wei and Medasani, Bharat and Qu, Xiaohui and Kocher, Michael and Brafman, Miriam and Petretto, Guido and Rignanese, Gian-Marco and Hautier, Geoffroy and Gunter, Daniel and Persson, Kristin A. , title =. Concurrency and Computation: Practice and Experience , volume =. doi:https://doi.org/10.1002/cpe.3505 , url =....

    work page doi:10.1002/cpe.3505

  43. [44]

    Chevrier, Kristin A

    Shyue Ping Ong and William Davidson Richards and Anubhav Jain and Geoffroy Hautier and Michael Kocher and Shreyas Cholia and Dan Gunter and Vincent L. Chevrier and Kristin A. Persson and Gerbrand Ceder , keywords =. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis , journal =. 2013 , issn =. doi:https://doi...

    work page doi:10.1016/j.commatsci.2012.10.028 2013

  44. [45]

    Scientific Data , author =

    Large language model enhanced corpus of. Scientific Data , author =. 2024 , note =. doi:10.1038/s41597-024-03180-9 , language =

    work page doi:10.1038/s41597-024-03180-9 2024

  45. [46]

    2025 , pages =

    ACS Catalysis , author =. 2025 , pages =. doi:10.1021/acscatal.5c05527 , language =

    work page doi:10.1021/acscatal.5c05527 2025

  46. [47]

    Scientific Reports , author =

    Catalytic behavior of metal catalysts in high-temperature. Scientific Reports , author =. 2017 , note =. doi:10.1038/srep41207 , language =

    work page doi:10.1038/srep41207 2017

  47. [49]

    Solid State Sciences , author =

    Indium decorated nanoporous. Solid State Sciences , author =. 2022 , pages =. doi:10.1016/j.solidstatesciences.2022.106916 , language =

    work page doi:10.1016/j.solidstatesciences.2022.106916 2022

  48. [51]

    Green Chemistry , author =

    Ru nanoparticles stabilized by ionic liquids supported onto silica: highly active catalysts for low-temperature. Green Chemistry , author =. 2018 , note =. doi:10.1039/C8GC02337A , language =

    work page doi:10.1039/c8gc02337a 2018

  49. [52]

    The Journal of Physical Chemistry Letters , author =

    Tuning. The Journal of Physical Chemistry Letters , author =. 2020 , note =. doi:10.1021/acs.jpclett.0c02011 , number =

    work page doi:10.1021/acs.jpclett.0c02011 2020

  50. [53]

    Boosting

    Chai, Shanshan and Men, Yong and Wang, Jinguo and Liu, Shuang and Song, Qiaoling and An, Wei and Kolb, Gunther , month = oct, year =. Boosting. Journal of. doi:10.1016/j.jcou.2019.05.031 , urldate =

    work page doi:10.1016/j.jcou.2019.05.031 2019

  51. [55]

    ACS Catalysis , author =

    Flame. ACS Catalysis , author =. 2025 , pages =. doi:10.1021/acscatal.5c02380 , language =

    work page doi:10.1021/acscatal.5c02380 2025

  52. [56]

    Catalysis Science & Technology , author =

    Highly active. Catalysis Science & Technology , author =. 2016 , note =. doi:10.1039/C6CY01252C , language =

    work page doi:10.1039/c6cy01252c 2016

  53. [57]

    The Journal of Physical Chemistry C , author =

    Kinetic and. The Journal of Physical Chemistry C , author =. 2013 , note =. doi:10.1021/jp401566m , number =

    work page doi:10.1021/jp401566m 2013

  54. [58]

    Applied Surface Science , author =

    Temperature-dependent surface free energy and the. Applied Surface Science , author =. 2020 , pages =. doi:10.1016/j.apsusc.2019.144859 , language =

    work page doi:10.1016/j.apsusc.2019.144859 2020

  55. [59]

    Nano Letters , author =

    Wulff. Nano Letters , author =. 2011 , note =. doi:10.1021/nl2018146 , number =

    work page doi:10.1021/nl2018146 2011

  56. [60]

    ACS Catalysis , author =

    How. ACS Catalysis , author =. 2025 , pages =. doi:10.1021/acscatal.5c02008 , language =

    work page doi:10.1021/acscatal.5c02008 2025

  57. [61]

    Catalysis Science & Technology , author =

    Scaling relations between adsorption energies for computational screening and design of catalysts , volume =. Catalysis Science & Technology , author =. 2014 , note =. doi:10.1039/C4CY00335G , language =

    work page doi:10.1039/c4cy00335g 2014

  58. [62]

    Accounts of Chemical Research , author =

    Combining. Accounts of Chemical Research , author =. 2020 , note =. doi:10.1021/acs.accounts.0c00340 , number =

    work page doi:10.1021/acs.accounts.0c00340 2020

  59. [63]

    npj Computational Materials , author =

    A high-throughput framework for determining adsorption energies on solid surfaces , volume =. npj Computational Materials , author =. 2017 , note =. doi:10.1038/s41524-017-0017-z , language =

    work page doi:10.1038/s41524-017-0017-z 2017

  60. [64]

    Joule , author =

    Accelerated discovery of. Joule , author =. 2025 , note =. doi:10.1016/j.joule.2025.102213 , language =

    work page doi:10.1016/j.joule.2025.102213 2025

  61. [65]

    Arrival time detection of ae signals using multi-threshold wavelet denoising and improved aic for composites damage localization

    Feng, Yuqing and Chen, Yushun and Zheng, Li and Chen, Xuetao and Li, Tan and Zhao, Wenbo , month = aug, year =. Descriptors construction and application in catalytic site design , volume =. iScience , publisher =. doi:10.1016/j.isci.2025.113080 , language =

    work page doi:10.1016/j.isci.2025.113080 2025

  62. [67]

    Journal of CO2 Utilization , author =

    Thermal, electrochemical, and photochemical conversion of. Journal of CO2 Utilization , author =. 2013 , keywords =. doi:10.1016/j.jcou.2013.03.004 , urldate =

    work page doi:10.1016/j.jcou.2013.03.004 2013

  63. [68]

    Journal of Catalysis , author =

    From the. Journal of Catalysis , author =. 2015 , keywords =. doi:10.1016/j.jcat.2014.12.033 , urldate =

    work page doi:10.1016/j.jcat.2014.12.033 2015

  64. [69]

    Journal of Physics: Condensed Matter , author =

    Using scaling relations to understand trends in the catalytic activity of transition metals , volume =. Journal of Physics: Condensed Matter , author =. 2008 , pages =. doi:10.1088/0953-8984/20/6/064239 , language =

    work page doi:10.1088/0953-8984/20/6/064239 2008

  65. [70]

    , month = mar, year =

    Sun, Ruiyan and Liao, Yuhe and Bai, Shao-Tao and Zheng, Mingyuan and Zhou, Cheng and Zhang, Tao and Sels, Bert F. , month = mar, year =. Heterogeneous catalysts for. Energy & Environmental Science , publisher =. doi:10.1039/D0EE03575K , language =

    work page doi:10.1039/d0ee03575k

  66. [71]

    Journal of Cleaner Production , author =

    Meta-analysis approach for understanding the characteristics of. Journal of Cleaner Production , author =. 2022 , pages =. doi:10.1016/j.jclepro.2022.130653 , language =

    work page doi:10.1016/j.jclepro.2022.130653 2022

  67. [72]

    Chem , author =

    Disentangling the activity-selectivity trade-off in. Chem , author =. 2026 , keywords =. doi:10.1016/j.chempr.2026.102942 , language =

    work page doi:10.1016/j.chempr.2026.102942 2026

  68. [73]

    Hu, B.; Guild, C.; Suib, S. L. Thermal, electrochemical, and photochemical conversion of CO2 to fuels and value-added products. Journal of CO2 Utilization 2013, 1, 18--27

    work page 2013

  69. [74]

    D.; Zhong, Q.; Wang, Y.; Russell, C

    Ye, R.-P.; Ding, J.; Gong, W.; Argyle, M. D.; Zhong, Q.; Wang, Y.; Russell, C. K.; Xu, Z.; Russell, A. G.; Li, Q.; Fan, M.; Yao, Y.-G. CO _2 hydrogenation to high-value products via heterogeneous catalysis. Nature Communications 2019, 10

    work page 2019

  70. [75]

    How to Measure the Reaction Performance of Heterogeneous Catalytic Reactions Reliably

    Zhang, M.; Wang, M.; Xu, B.; Ma, D. How to Measure the Reaction Performance of Heterogeneous Catalytic Reactions Reliably . Joule 2019, 3, 2876--2883

    work page 2019

  71. [76]

    A.; Water, L

    Beck, A.; Newton, M. A.; Water, L. G. A. v. d.; Bokhoven, J. A. v. The Enigma of Methanol Synthesis by Cu / ZnO / Al2O3 - Based Catalysts . Chemical Reviews 2024, Publisher: American Chemical Society

    work page 2024

  72. [77]

    M.; Thonemann, N.; Beale, A

    Ye, J.; Dimitratos, N.; Rossi, L. M.; Thonemann, N.; Beale, A. M.; Wojcieszak, R. Hydrogenation of CO2 for sustainable fuel and chemical production. Science 2025, 387, eadn9388, Publisher: American Association for the Advancement of Science

    work page 2025

  73. [78]

    Meta-analysis approach for understanding the characteristics of CO2 reduction catalysts for renewable fuel production

    Bahri, S.; Pathak, S.; Singhahluwalia, A.; Malav, P.; Upadhyayula, S. Meta-analysis approach for understanding the characteristics of CO2 reduction catalysts for renewable fuel production. Journal of Cleaner Production 2022, 339, 130653

    work page 2022

  74. [79]

    Chem 2026, 102942

    Zada, H.; Yu, J.; Fang, C.; Jian Sun Disentangling the activity-selectivity trade-off in CO2 hydrogenation to methanol. Chem 2026, 102942

    work page 2026

  75. [80]

    From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

    Benavides-Hernández, J.; Dumeignil, F. From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design. ACS Catalysis 2024, 14, 11749--11779

    work page 2024

  76. [81]

    D.; Patience, G

    Ortega, C.; Otyuskaya, D.; Ras, E.; Virla, L. D.; Patience, G. S.; Dathe, H. Experimental methods in chemical engineering: High throughput catalyst testing — HTCT . The Canadian Journal of Chemical Engineering 2021, 99, 1288--1306

    work page 2021

  77. [82]

    L.; Devi, R

    Dongapure, P.; Tekawadia, J.; Thundiyil, S.; Caha, I.; Deepak, F. L.; Devi, R. N. Mechanistic Insights into Near Ambient Pressure Activity of Intermetallic NiZn / TiO2 Catalyst for CO2 Conversion to Methanol . ChemCatChem 2023, 15, e202201150, \_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cctc.202201150

    work page doi:10.1002/cctc.202201150 2023

  78. [83]

    J.; Vojvodic, A.; Hummelshøj, J

    Medford, A. J.; Vojvodic, A.; Hummelshøj, J. S.; Voss, J.; Abild-Pedersen, F.; Studt, F.; Bligaard, T.; Nilsson, A.; Nørskov, J. K. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis. Journal of Catalysis 2015, 328, 36--42

    work page 2015

  79. [84]

    K.; Abild-Pedersen, F.; Studt, F.; Bligaard, T

    Nørskov, J. K.; Abild-Pedersen, F.; Studt, F.; Bligaard, T. Density functional theory in surface chemistry and catalysis. Proceedings of the National Academy of Sciences 2011, 108, 937--943

    work page 2011

  80. [85]

    Jones, G.; Bligaard, T.; Abild-Pedersen, F.; Nørskov, J. K. Using scaling relations to understand trends in the catalytic activity of transition metals. Journal of Physics: Condensed Matter 2008, 20, 064239

    work page 2008

Showing first 80 references.